Expanding artificial intelligence methods to the domain of structural damage detection can effectively enhance the detection accuracy and efficiency. Models like convolutional neural networks can effectively capture significant features related to structural damage. However, due to the receptive field constraints, it is difficult to fully capture the global characteristics of the data. In addition, considering structural damage identification and damage location as a single task will lead to a large number of label classifications, thereby increasing the requirements for model complexity and training data quality. To address these issues, a multiscale fusion-based multitask Transformer (MFMTT) model based on a multivariate time-series Transformer is proposed in this paper. The encoder structure of Transformer is employed to extract the global features of sequence data, and the structural damage detection task is divided into two subtasks, each of which has a lower number of categories than the single-task one. The proposed MFMTT model is validated using a numerical simulation dataset of a simply supported beam and the International Association for Structural Control – American Society of Civil Engineers (IASC-ASCE) phase II benchmark experimental dataset, ensuring reproducibility across both synthetic and real-world scenarios. The experimental results demonstrate that the MFMTT model achieves accurate identification of structural damage states across single-damage scenarios, multidamage conditions, and benchmark experimental setups, with damage detection accuracy exceeding 99%.
Jiang et al. (Tue,) studied this question.